Article
citation information:
Ahmad,
I., Hussain, I., Shah, S.T.H., Aamir, A., Zaman, K. Optimizing delivery routes,
enhancing supply chain efficiency, and investing in infrastructure: a strategic
approach to reducing carbon emissions from the transport sector. Scientific Journal of Silesian University of
Technology. Series Transport. 2025, 128,
5-26. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2025.128.1
Ishfaq AHMAD[1],
Ijaz HUSSAIN[2],
Syed Tahir Hussain SHAH[3],
Alamzeb AAMIR[4],
Khalid ZAMAN[5]
OPTIMIZING
DELIVERY ROUTES, ENHANCING SUPPLY CHAIN EFFICIENCY, AND INVESTING IN
INFRASTRUCTURE: A STRATEGIC APPROACH TO REDUCING CARBON EMISSIONS FROM THE
TRANSPORT SECTOR
Summary. The main objective of
this research is to get a better understanding of the carbon emissions produced
by last-mile delivery, the impact of various vehicle types on these emissions,
effective route efficiency, the role of urban infrastructure, and the optimization
strategies in reducing emissions in Pakistan's transportation sector by
using data from 1996 to 2022. It
examines economic and environmental benefits. The ARDL results show that
distribution route optimization reduces emissions over time, while alternative
energy consumption, distribution density, and infrastructure investment reduce
transportation emissions. Optimizing delivery routes reduced transportation
emissions over time, demonstrating the importance of sustainable logistics in
environmental issues. Granger causality estimations show that delivery density
affects route optimization, infrastructure investment, supply chain efficiency,
and alternative energy use. This shows how environmental sustainability methods
rely on one another. A variance decomposition analysis indicates that
alternative energy consumption, distribution density, and infrastructure
investment will likely affect transport emissions variance over time. The
research recommends that logistics businesses, governments, and politicians
improve last-mile delivery operations and dramatically cut carbon emissions.
The study provides practical solutions to environmental issues in urban goods
transportation in Pakistan and advances sustainable logistics management.
Keywords: transport emissions, last mile delivery, route optimization, delivery
density, supply chain efficiency, infrastructure investment, ARDL technique
1. INTRODUCTION
In the current age of environmental concerns and climate change, the
transport sector is under more scrutiny because of its greenhouse gas
emissions. Energy efficiency and transport emissions are major challenges for
this business [1]. The global transportation network powers the modern economy.
Thus, delivery routes must be efficient. [2] argue that this optimization is
essential to reduce the transportation sector's environmental impact and make
logistical operations more profitable. Studies suggest that feeder buses and
their rapid transit systems reduce oil and traffic, improving energy
efficiency. Predictive algorithms and smart meters can optimize delivery routes
and reduce redelivery, saving time and energy. [3] found that energy-saving
strategies for automated guided vehicles, hybrid optimization algorithms for
vehicle routing problems, and non-intrusive energy optimization for industrial
robots could reduce energy consumption and increase efficiency. Deep
reinforcement learning systems also optimize delivery paths and efficiency [4].
In the long term, optimal transportation distribution routes may enhance Asian
energy efficiency.
Vehicle pollution is substantially impacted by urban transportation
density. [5] found that longer work trip travel times and urban density-induced
traffic congestion increase emissions. Although denser cities have lower
emissions per capita, population density reduces emissions. Another purpose of
urban planning and design is to reduce car usage, transport, and greenhouse gas
emissions. In regions with high job and residential density and good public
transit, commuters may travel less and use fewer cars. Higher urban transport
density has positives and downsides for transportation emissions [6].
Addressing these positives and downsides requires sustainable urban planning
and transportation strategies. A "bottom-up" theoretical computation
of transport carbon emissions and a basic distribution model of intermodal land
use near rail transit stations may boost urban transport density and reduce
emissions [7]. A linear model based on a complex network approach can predict
how emissions would affect urban street network air pollution. Innovative urban
space management, such as UAVs with mobile vision systems, will enhance
transport management and reduce emissions. [8] suggests adding freight-specific
data to transport models and imposing regulatory limits to reduce urban freight
CO2 emissions. When combined, these strategies may reduce trip
emissions and increase urban transportation density.
Hydrogen technologies, energy-efficient, low-emitting cars, and their
electrification or hybridization reduce transportation emissions. [9] suggests
that these decisions may reduce local emissions and global warming. Non-coal
fuels should be utilized to reduce emissions and improve public health.
Emissions control and carbon pricing may decrease air pollutants, including CO2.
Due to the impact of economic development and energy consumption on goods
movement, comprehensive energy and environmental restrictions are needed [10].
Emerging countries reduce emissions by increasing public transport usage. The
rapid adoption of electric vehicles (EVs), standardized charging
infrastructure, and government promotions may reduce tailpipe emissions.
Supply chain efficiency reduces carbon emissions in city delivery's
final stages. A well-managed supply chain allows stakeholders to cooperate,
resulting in optimal routing, reduced congestion, and improved logistical
performance [11]. Carbon emissions decrease due to more efficient vehicle
utilization and less damaging delivery routes. Supply chain efficiency is
essential for reducing urban carbon emissions during late delivery. Smart
transport systems and sophisticated logistical equipment may enhance road
safety, traffic management, and real-time tracking [12]. Energy-efficient
products and flexible production may reduce supply chain environmental impacts
and energy consumption. A carbon emissions taxation scheme that considers
supply chain power structures and cost efficiency is needed to meet
sustainability targets. The manufacturer's contract design may reduce carbon
abatement knowledge asymmetry losses [13].
The interaction prompted these research questions.
First, how do
distribution density-optimized supply routes affect carbon emissions?
Efficient routes and increasing delivery density led to more condensed
deliveries, which decreases emissions. This might reduce the environmental
impact of each delivery to its utmost. Secondly, how do the utilization of alternative energy sources and investments in
infrastructure affect the efficiency of the supply chain, and subsequently, how do they influence the carbon emissions
associated with last-mile delivery? The reduction of carbon emissions in
the final mile of delivery might be achievable through supply chain efficiency
enhancements brought about by strategic infrastructure investments and the
adoption of alternative energy sources. Lastly, how does the level of investment in urban infrastructure influence the
connection between vehicle type and carbon emissions? Strategic
infrastructure investments, especially those supporting alternative energy
usages, may influence the effectiveness of different vehicle types in achieving
sustainable last-mile delivery practices, considering the unique urban environments
in Pakistan. The study has the following
research objectives, i.e.,
I.
Analyze the impact of delivery route optimization
and density on carbon emissions in last-mile delivery in Pakistan.
II. Investigate the influence of
alternative energy consumption and infrastructure investment on supply chain
efficiency and carbon emissions in last-mile delivery in metropolitan areas of
Pakistan.
III. Examine the effects of urban
infrastructure investment on vehicle types and carbon emissions in Pakistan,
focusing on alternative energy consumption.
After the introduction, Section 2 presents the literature review.
Methodology is outlined in Section 3. Results are detailed in Section 4.
Finally, Section 5 concludes the study.
2. LITERATURE REVIEW
Delivery route optimization is essential for lowering carbon emissions
and other environmental issues. [14] found that improving express delivery
station distribution routes may minimize carbon emissions. It can minimize
pollutants and carbon emissions while delighting consumers and maximizing
economic profits by improving cold-chain vehicle distribution patterns.
Optimizing trash collection routes may reduce operational costs and carbon
emissions. Environmental expenses and delivery delay penalties must be
considered while optimizing low-carbon logistics delivery routes. This reduces
delivery costs and carbon emissions [15]. Optimizing routes can save costs and
emissions in carbon tax programs. This applies notably to delivery and pickup
trucks working together. For these reasons, improving delivery routes reduces
carbon emissions. It has many other positive effects on cold-chain
distribution, express delivery stations, and waste management systems, which
promote economic growth and ecological stability. According to [16],
electric bus charging stations may be strategically located to minimize
operating costs and emissions. [17] examines whether direct measurement
technology can monitor port emissions for accurate carbon accounting and
regulatory compliance. It investigates novel monitoring techniques to reduce
carbon emissions. The first hypothesis of the study is as follows:
H1: Delivery route optimization is anticipated
to result in a reduction of carbon emissions.
Delivery density considerably impacts carbon emissions. Delivery vehicle
carbon emissions may be reduced by increasing road density or reducing
warehouse distance. The CO₂ efficiency of delivery services compared to
personal transport depends on the emissions ratio and customer density.
Personal travel is best for a few consumers and has low emissions, whereas
delivery services are best for many customers and have low emissions. More
customers per car are needed to build more prosperous routes. Ground vehicles
become more energy-efficient as deliveries grow [18]. A Chinese study reveals a
U-shaped relationship between per capita express delivery volumes and
transportation sector CO₂ emissions, suggesting an optimal delivery
density to reduce emissions [19]. Consolidating distribution density reduces
emissions and costs. Distribution density optimization employing strategic
techniques reduces carbon emissions significantly and may minimize
transportation-related carbon emissions. The second hypothesis of the study
is as follows:
H2: An increase in delivery density is expected
to correlate with higher carbon emissions due to the elevated energy
consumption inherent in logistics activities.
Alternative energy affects carbon emissions considerably. Increasing
renewable energy use may reduce carbon emissions per capita over time. [20]
found that economic growth and nonrenewable energy
aggravate environmental degradation, whereas renewable energy reduces it. These
findings emphasize the need to lower carbon emissions and prevent climate
change by promoting alternative energy sources. The third hypothesis of
the study is as follows:
H3: The utilization of alternative energy
sources within logistics activities is projected to improve efficiency and
subsequently decrease carbon emissions.
GHG emissions are greatly affected by supply chain efficiency. Supply
chain components may reduce their environmental impact and energy consumption
by 50% by employing more energy-efficient products, more flexible production
methods, and better product quality [21]. Sustainable supply chain development
and shared facility investments reduce carbon emissions and boost income.
Supply chain management that considers production and transportation carbon
emissions may enhance solutions and decrease emissions. Improved supply chain
efficiency reduces carbon emissions. Hence, efficient coordination throughout
the supply chain system may help meet emissions objectives [22]. These findings
demonstrate the importance of supply chain efficiency for sustainable operations
and carbon reduction. The fourth hypothesis of this study is as follows:
H4: Enhancing supply chain efficiency is
anticipated to lead to a reduction in carbon emissions.
Carbon emissions are strongly impacted by infrastructure spending.
Building information infrastructure may reduce emissions directly and
indirectly via technology innovation. Rail infrastructure has a negligible
impact on carbon emissions, whereas air transport infrastructure significantly
increases them [23]. [24] found increasing CO₂ emissions due to the
relationship between rail transport infrastructure and GDP. Cities that
efficiently invest in infrastructure have fewer carbon emissions and greater
production. However, inefficiently spending cities emit more carbon and produce
less. Additionally, rigorous environmental rules are essential to achieve
emissions reduction objectives during significant public infrastructure
investment. Strict environmental rules, public infrastructure coordination, and
information infrastructure building may harness infrastructure investment to
reduce carbon emissions [25]. Despite sizeable public infrastructure
investments, rigorous environmental restrictions are needed to meet carbon
reduction objectives. Investment in information infrastructure reduces the
direct and indirect impacts of technological innovation on carbon emissions.
[26] found that coordinating public infrastructure supply with environmental
regulations may reduce emissions and pollutants. Investments in renewable
energy, energy storage, clean mobility, carbon capture, and zero-emission power
generation may help decarbonize and reduce carbon emissions. By using
climate-smart infrastructure and sustainable practices, infrastructure
developments may minimize carbon emissions and climate change. The study's
final hypothesis is as follows:
H5: Investments in infrastructure are expected
to have a mitigating effect on carbon emissions.
Table 1 shows the research gaps extracted from the past literature.
Tab. 1
Research Gaps and Study Contributions Based on Previous Literature
Support
|
Authors |
Research gaps and study’s
contribution |
|
[27] |
The referenced article leans towards technological advancements for
traffic forecasting, while our study places a greater emphasis on the
sustainability aspects of last-mile delivery operations. The
study extends beyond technological advancements in traffic forecasting by
emphasizing the sustainability aspects of last-mile delivery operations,
particularly investigating the impact of vehicle types and route efficiency
on carbon emissions reduction. |
|
[28] |
While the
referenced study focuses on route optimization using a modified Ant Colony
Optimization algorithm, our study takes a broader approach, exploring the
interplay of multiple factors, including vehicle types and urban
infrastructure, for sustainable last-mile delivery operations. |
|
[29] |
The
referenced study concentrates on the operational challenges of incorporating
new mobility-assist elements into e-grocery delivery, addressing vehicle
routing complexities with a focus on mixed vehicles and load-dependent
considerations. Our study analyzed the role of
delivery route density and infrastructure investment to move forward towards
the decarbonization agenda. |
|
[30] |
The
referenced study focused on developing a methodology for prioritizing best
practices in a Brazilian parcel delivery service as a case study. Our study
differs in its specific objectives and approaches for analyzing
sustainable last-mile delivery optimization, which are lacking in the cited
study. |
|
[31] |
While the
cited study focuses on route optimization in heterogeneous fleets, our study
explores sustainability aspects and the interplay of various factors,
broadening the scope beyond vehicle routing to include urban infrastructure
and environmental considerations. |
|
[32] |
The reference study has a broader focus on reviewing trends in
environmentally sustainable solutions for urban last-mile deliveries in the
e-commerce market. Our study takes a more focused and investigative approach
by exploring the interplay of factors related to green supply chain
efficiency and route optimization in designing for a carbon neutrality
agenda. |
|
[33] |
In contrast
to the integration-focused approach of the referenced study, our research
emphasizes sustainable last-mile delivery operations by examining the impact
of vehicle types and route efficiency on carbon emissions reduction,
contributing unique insights to the field. |
|
[34] |
Our study
builds upon the challenges identified in the referenced literature by
investigating last-mile delivery optimization strategies under stochastic
travel times, offering novel solutions to address uncertainties in delivery
operations. |
|
[35] |
Our study
complements the vehicle routing-focused approach of the cited study by
exploring green supply chain management processes for improving logistics
activities, providing valuable insights into sustainable last-mile delivery
operations. |
|
[36] |
The referenced study presents a literature review that focuses on
sustainability practices in urban routing and identifies gaps in the related
literature and suggests directions for future research, concluding that the
economic dimension is the prominent driver among the three pillars of
sustainability. Investigating the interaction of many aspects in last-mile
delivery optimization, our research takes a deep and exploratory approach. |
|
[37] |
Our research
promotes sustainable last-mile delivery operations by considering factors
like carbon emissions reduction and urban infrastructure, unlike the
referenced study's focus on strategy and solution selection. |
2.1. Theoretical Framework
2.1.1. Pigouvian Taxation (or
Pigouvian Subsidies)
Emission
taxes, a kind of Pigovian taxation, may decrease
pollution by considering external costs and encouraging emission reduction.
Making pollution charges internally motivates polluters to cut emissions and
utilize greener technologies [38]. Group size and communication dynamics affect
Pigovian taxes like the "average Pigouvian
tax" (APT). Battery electric vehicle (BEV) traffic restriction exemptions
are Pigovian taxes that promote uptake and
sustainability. Pigovian tariffs reduce fossil fuel
usage by reflecting the social and environmental costs of waste. However, tax
rates and subsidies must be adjusted, and customized taxes are impracticable
[39]. Many Pigovian tax versions have been proposed
to address carbon emissions and social issues. A carbon tax strategy maximizes
social welfare and strives to improve society and individual conduct.
Multi-agent reinforcement learning methods like the Learning Optimum Pigovian Tax (LOPT) may enhance societal welfare by
approaching optimum taxes [40]. Pigovian taxes may
cut carbon emissions and address social concerns.
2.1.2. Porter's Environmental Hypothesis
The
Porter hypothesis states that well-designed environmental limitations may
increase corporate performance and innovation, although its significance varies
by location. Environmental policy bribery in underdeveloped nations may reduce
regulatory costs and boost innovation, defying Porter's theory [41]. Tax
rebates and incentives in China may boost green investment among enterprises
affected by environmental restrictions. Despite an increase in local green
patents, China's urban environmental policies did not increase green total
factor production. Well-designed environmental policies may increase
technological innovation and competitiveness. [42] suggest that environmental
rules may enhance a company's environmental investment and profitability,
particularly for socially backed activities. Environmental restrictions may
impact industrial innovation. However, environmental policy bribes might damage
regulatory measures without detection [43]. Environmental regulations may
affect environmentally aware innovation as political institutions grow. Even if
environmental regulations increase green patents, factor productivity may not
increase. Legal restrictions may affect the total production of the green
factor. Porter's hypothesis has pros and cons when applied to how environmental
limits affect innovation and productivity.
Based
on the mentioned theoretical framework, Figure 1 shows the conceptual framework
of the study for ready reference.
Figure
1 illustrates that achieving sustainable last-mile delivery optimization
entails reducing carbon emissions associated with delivery route optimization,
delivery density, and alternative energy usage. Supply chain efficiency and
infrastructure investment act as mediators in this relationship, facilitating
progress toward the decarbonization agenda.
Fig.1. Conceptual Framework
3. DATA AND METHODOLOGY
The
study includes the following variables for empirical analysis, i.e.,
Dependent Variable:
- Carbon
Emissions from Transport (TCO2): This crucial variable measures last-mile delivery CO2
emissions. The World Development Indicators database provides statistics on
transportation CO₂ emissions as a % of total fuel use [44].
Independent Variables:
1)
Delivery Route Optimization (DRO): It measures
delivery route efficacy by average distance, number of stops, and delivery
time. Transport Efficiency (% of commercial service exports) is an indirect
delivery route optimization statistic that is used in this study.
2)
Delivery Density (DD): Higher distribution density
may enhance delivery efficiency and carbon emissions. The urban population
density, measured in people per square kilometer, indicates a region's density.
It shows a city's population density and delivery options. The requirement for
last-mile delivery services is correlated with urban population density, which
influences their efficacy and environmental impact.
3)
Alternative Energy Usage (AEU): The proportion of a
country's or region's energy consumption from renewable sources indicates how
much electricity we utilize and how many green technologies are employed. Green
energy usage has declined since transitioning to more efficient and renewable
energy sources reduces transportation emissions. Thus, the study used renewable
energy consumption (% of total energy use) as a proxy for the AEU variable.
4)
Supply Chain Efficiency (SCE): It indicates a supply
chain's ability to optimize resources and processes, which impacts
environmental sustainability. SCE optimizes transport routes to reduce carbon
emissions by reducing unnecessary mileage and fuel use. Sustainable practices,
simpler customs processes, and cutting-edge technology contribute to SCE. The
goal of high SCE is an efficient supply chain that maximizes economic
efficiency and minimizes environmental impact. The logistics performance
indicator is used as a proxy for SCE in this study.
5)
Infrastructure Investment (INVEST): Quality urban
infrastructure, including roads and traffic management, may affect mobility and
carbon emissions. Gross Fixed Capital Formation (GFCF, constant 2015 US$)
represents the total value of fixed assets, such as buildings, infrastructure,
and equipment, that are used for production. The higher the GFCF,
infrastructure, the more infrastructure improvements may increase or decrease
carbon emissions.
Pakistan
has several economic sectors, making it a booming economy. Researching emerging
economies is essential due to their unique opportunities and risks. Pakistan's
urbanization, infrastructural constraints, and population dispersion make
last-mile delivery difficult. [45] found that last-mile distribution is vital
in emerging countries like Pakistan. Due to its involvement in global supply
chains, notably in textiles and manufacturing, the country's last-mile delivery
impact on international trade must be examined. Pakistan's study may assist in
understanding last-mile distribution's environmental implications and create
sustainable approaches. [46] note that expanding markets like Pakistan's
last-mile deliveries reveal how mobile applications and other transportation
modes are being implemented. Additionally, selecting Pakistan's economy
involves other crucial factors: a) Pakistan's economic diversity may help us
understand how economic sectors and regions affect last-mile delivery [47], b)
Pakistan, a South Asian country, may provide insight into how regional
connectivity, trade agreements, and cross-border logistics affect last-mile
delivery [48], c) Consumer behavior, preferences, and
demands impact last-mile delivery strategies and efficiency; Pakistan's diverse
and large population may provide insight into these aspects [49], and d)
Pakistani last-mile delivery technology illuminates technological advances,
challenges, and successful methods.
3.1. Econometric Framework
The
Augmented Dickey-Fuller (ADF) unit root test, crucial for empirical
demonstrations, was used to determine stationarity from time series data. The
basic ADF test in time series analysis may determine whether a series is
stationary. This evaluation is essential for accurate forecasting. After data
differencing, we may test the stationarity hypothesis if the time series does
not have a unit root. For regression analysis modeling
tool selection, the ADF test determines data trendiness or stochasticity. Policymaking
and strategic planning benefit from the ADF test's illumination of long-term
economic determinants. Equations (1) to (7) show the ADF formulation of the
given model, i.e.,
(1)
(2)
(3)
(4)
(5)
(6)
Where,
TCO2: Carbon emissions from transport
DRO: Delivery route optimization
DD: Delivery density
AEU: Alternative energy use
SCE: Supply chain efficiency
INVEST: Infrastructure investment, and
∆, t, and ƹ show difference operator,
time, and error term, respectively.
[50-51]
introduced ARDL models, which have various advantages over other methods.
Unlike other cointegration approaches, the ARDL does not need all variables to
be integrated in the same sequence. Any order of integration – fractionally
integrated, zero-order, and one-order – can be used with the ARDL approach.
Second, unlike sensitive cointegration approaches, the ARDL test may be
employed with small samples. Thirdly, the ARDL technique, known for its
reliability, generally yields unbiased long-run model estimates and reliable
t-statistics even with endogenous regressors. [51] made many assumptions while
developing limits testing. The dependent variable must equal I(1), there must
be no degenerate circumstances, and the independent variables must not be
external to the model. A generalized F-test for all lagged level variables and
a t-test for the dependent variable's lagged level were suggested by [51] as
cointegration tests. These tests must assume an I(1) dependent variable to
avoid degenerate scenarios. Degenerate occurrences arise when the error
correction term's dependent or independent variable lagged levels are
statistically irrelevant. In the degenerate lagged dependent variable and
independent variable(s) cases, the delayed values of the dependent and
independent variables are less essential. Cointegration is incorrect because
this partial error correction factor leaves the residual gap unbridged.
Overall, the F-test significance implies that the lag levels of the variables
are jointly significant when executing the limits test. Lagged levels of the
dependent variable or independent variable may explain the F-test's statistical
significance. A t-test for the dependent variable's lagged level is needed to
rule out a degenerate lagged dependent variable. Assuming the dependent
variable is I(1), supplemental eliminates degenerate lagged independent
variable(s). The lagged level dependent variable must be substantial for the
ARDL equation to become a Dickey-Fuller equation. This delayed dependent
variable term's relevance shows I(0) is the dependent variable. Finally, [51]
introduced the ARDL limits cointegration evaluation approach. Traditional
cointegration tests do not support regressors with unknown or mixed integration
orders, I(0) or I(1), while this technique does. However, the ARDL limits test
may show degenerative non-cointegration. Equation 7 shows the ARDL model
specification, i.e.,
(7)
Where Δ shows
difference operator.
The
ARDL paradigm requires all variables' lagged coefficients to be significant for
an unconstrained error correction equation to be cointegrated. The general
F-statistic relevance does not establish cointegration since it does not
exclude degenerate cases. A t-test on the dependent variable's lagged level may
identify the degenerate lagged dependent variable scenario in Pesaran et al.'s ARDL limits test. If I(1) is the dependent
variable, degenerate lagged independent variables are irrelevant. This may be used
alongside [51]'s other two tests to examine the degenerate lagged independent
variable(s) scenario. By loosening the assumption of an I(1) dependent
variable, we may assess cointegration and delayed independent variable tests:
- H0:
δ1 = δ2 = δ3 = δ4
= δ5= 0
- H1:
δ1 ≠ δ2 ≠ δ3
≠ δ4 ≠ δ5≠ 0
Equation
(8) shows the error correction term (ECT) within the ARDL formulation for
robust inferences.
(8)
Where 
shows the adjustment parameter.
Equation
(9) shows the VAR specification of Granger causality specification to assess
cause-and-effect relationships between the studied variables, i.e.,
(9)
Equations
(10) to (15) show multivariate Granger causality system, i.e.,
(10)
(11)
(12)
(13)
(14)
(15)
The
study applied impulse response function (IRF) and variance decomposition
analysis (VDA) to assess the direction and magnitude between the variables for
the next 10 years’ time period. The variance shocks over the time period are
accessed by the variables used in the study, which helps to suggest
inter-temporal policy implications for the country.
4. RESULTS AND DISCUSSION
Table 2 provides the
descriptive statistics of the study variables. For transport emissions (TCO2),
the mean value is approximately 27.735 metric tons per kilometer,
with a standard deviation of 1.599. The distribution exhibits negative skewness
(-1.260) and positive kurtosis (3.699), indicating a left-skewed distribution
with heavier tails and greater peakness than a normal
distribution.
Tab. 2
Descriptive Statistics
|
Methods |
TCO2 |
AEU |
DD |
DRO |
INVEST |
SCE |
|
Mean |
27.735 |
47.956 |
66068466 |
42.472 |
5.13E+08 |
2.393 |
|
|
Maximum |
29.701 |
53.130 |
88979079 |
67.524 |
1.20E+09 |
2.697 |
|
|
Minimum |
23.268 |
42.100 |
44041395 |
11.083 |
17000000 |
2.080 |
|
|
Std. Dev. |
1.599 |
2.916 |
13387792 |
16.818 |
5.08E+08 |
0.183 |
|
|
Skewness |
-1.260 |
0.040 |
0.001 |
-0.565 |
0.573 |
0.383 |
|
|
Kurtosis |
3.699 |
2.214 |
1.836 |
2.098 |
1.463 |
2.477 |
|
Alternative energy use (AEU)
has a mean of approximately 47.956 megawatt-hours and a standard deviation of
2.916. The skewness is close to zero (0.040), suggesting a relatively
symmetrical distribution, while the kurtosis (2.214) indicates less peakness compared to carbon emissions. Delivery density
(DD), measuring the number of deliveries per unit area, has a mean of
approximately 66,068,466 with a standard deviation of 13,387,792. Both skewness
and kurtosis values are close to zero, indicating an approximately symmetrical
distribution with less peakness compared to variables
like carbon emissions and alternative energy use. Delivery route optimization
(DRO), representing the average distance traveled per
delivery, has a mean of approximately 42.472 kilometers
and a standard deviation of 16.818. The distribution shows slight left-skewness
(-0.565) and less peakness (kurtosis = 2.098)
compared to carbon emissions. Infrastructure investment (INVEST) has a mean of
approximately 5.13E+08 (5.13 billion) and a standard deviation of 5.08E+08
(5.08 billion). The skewness (0.573) and kurtosis (1.463) values suggest a
slightly right-skewed distribution with less peakness
compared to carbon emissions. Regarding supply chain efficiency (SCE), the mean
turnover rate is approximately 2.393 with a standard deviation of 0.183. The
skewness (0.383) and kurtosis (2.477) indicate a slightly right-skewed
distribution with greater peakness compared to a
normal distribution.
Table 3 shows the ADF unit root
estimates and reveals that both the TCO2 and AEU variables display
non-stationarity at the intercept level, indicating a trend component in their
original series. However, after taking the first difference, both CO2
and AEU series become stationary, suggesting they are integrated of order 1
(I(1)).
Tab. 3
Unit Root Estimates
|
Variables |
Level |
First difference |
Decision |
||
|
Intercept |
Intercept and Trend |
Intercept |
Intercept and Trend |
||
|
TCO2 |
-1.384 (0.574) |
-1.604 (0.764) |
-4.313 (0.002) |
-4.288 (0.012) |
I(1) |
|
AEU |
-1.832 (0.358) |
-2.689 (0.249) |
-3.873 (0.007) |
-3.896 (0.028) |
I(1) |
|
DD |
1.576 (0.999) |
-5.343 (0.001) |
-3.856 (0.008) |
-3.451 (0.070) |
I(0) |
|
DRO |
0.528 (0.984) |
-3.321 (0.085) |
-4.738 (0.001) |
-4.807 (0.004) |
I(0) |
|
INVEST |
-1.502 (0.517) |
-1.227 (0.883) |
-5.189 (0.000) |
-5.248 (0.001) |
I(1) |
|
SCE |
-2.370 (0.159) |
-2.272 (0.433) |
-4.804 (0.001) |
-4.740 (0.005) |
I(1) |
A small bracket shows the probability value.
In contrast, the DD and DRO variables demonstrate stationarity at the
intercept level, implying no trend component in their original series.
Additionally, both the INVEST and SCE variables exhibit non-stationarity at the
intercept level but achieve stationarity after differencing, indicating they
are also integrated of order 1 (I(1)).
Table 4 displays the lag length selection criteria and reveals that the
likelihood ratio (LR), FPE, AIC, SC, and HQ for lag 1 are all statistically
significant, suggesting that incorporating a lag enhances the model's fit
compared to a lag of 0. Therefore, based on the lag length selection criteria,
the study employed lag 1 for ARDL estimation.
Tab. 4
Lag Length Selection Criteria
|
Lag |
LogL |
LR |
FPE |
AIC |
SC |
HQ |
|
0 |
-1168.145 |
NA |
6.76e+31 |
90.31883 |
90.60916 |
90.40244 |
|
1 |
-978.4921 |
277.1848* |
5.40e+26* |
78.49939* |
80.53170* |
79.08462* |
* indicates
lag order selected by the criterion
ARDL
estimates' short- and long-run coefficients are in Table 5. A negative and
statistically significant coefficient for the error correction term (-0.438; p
= 0.0012) suggests a 43.8% correction per year. ARDL calculations help us
understand how explanatory variables impact transport emissions (TCO2) in the
short and long run. When using alternative energy sources, the coefficient
reduces transportation emissions statistically. Alternative energy sources may
increase emissions due to carbon emissions during manufacturing or conversion.
However, the alternative energy sources employed may affect this connection
[52]. The delivery density coefficient shows a short-term positive and
statistically significant influence on transport emissions. Per capita express
delivery volumes initially boost emissions before decreasing, demonstrating
that China's express delivery company has increased carbon emissions. Denser
delivery operations need more trucks and fuel, which increases emissions. [53]
emphasize the importance of transport efficiency in sustainable urban
development. A negative association between carbon emissions and the delivery
route optimization coefficient is seen in the short term despite not being
statistically significant. Route optimization aims to reduce pollutants and
fuel consumption; though the results are reasonable, the efforts may have
needed to be revised or crucial factors disregarded. In the short run,
infrastructure expenditures reduce transport emissions statistically.
Energy-intensive construction and operation procedures may explain the link
between infrastructure investment and emissions. Infrastructure projects'
environmental implications may vary in scale [54].
Tab. 5
ARDL Estimates
|
Dependent
Variable: D(CO2) |
||||
|
Selected Model: ARDL(1, 1, 1, 1, 1, 1) |
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
D(AEU) |
0.318005 |
0.118542 |
2.682633 |
0.0179 |
|
D(DD) |
1.96E-06 |
4.83E-07 |
4.057836 |
0.0012 |
|
D(DRO) |
-0.031892 |
0.035537 |
-0.897431 |
0.3847 |
|
D(INVEST) |
1.16E-09 |
5.23E-10 |
2.219727 |
0.0435 |
|
D(SCE) |
0.297408 |
0.948027 |
0.313712 |
0.7584 |
|
CointEq(-1)* |
-0.438933 |
0.108023 |
-4.063316 |
0.0012 |
|
Long Run Coefficients |
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
AEU |
0.823807 |
0.158802 |
5.187639 |
0.0013 |
|
DD |
-1.29E-07 |
6.01E-08 |
-2.139052 |
0.0697 |
|
DRO |
-0.105009 |
0.037010 |
-2.837352 |
0.0251 |
|
INVEST |
4.49E-09 |
6.65E-10 |
6.744138 |
0.0003 |
|
SCE |
3.505123 |
1.815174 |
1.931012 |
0.0948 |
|
C |
-17.87753 |
12.52093 |
-1.427812 |
0.1964 |
The
long-run estimates examine the variables' equilibrium linkages and effects over
time. Alternative energy sources reduce transport emissions over time. This
shows that switching to renewable energy sources decreases emissions,
reflecting the decarbonizing power systems and balancing environmental
concerns, energy independence, and economic development [55]. Carbon emissions
and delivery density (DD) are strongly inversely related over time. Higher
delivery densities increase transportation-related emissions in the short term,
despite technological advances, consumer behavior
changes, and new urban planning methods that may reduce these environmental
impacts. For delivery route optimization, the coefficient shows a statistically
significant long-term negative effect on carbon emissions. Because it increases
distribution efficiency and reduces travel, it may reduce carbon emissions.
Route planning may reduce chain supermarket distribution expenses, including
travel and carbon emissions [56]. Joint delivery models may lower operational
costs, carbon emissions, and customer satisfaction by improving horizontal
cooperation and resource pooling across express businesses. Long-term route
optimization improves transport and logistics, reducing emissions and energy
use. Delivery route optimization reduces emissions and maintains the
environment by reducing wasted time and vehicle utilization. Long-term
infrastructure expenditures reduce transport emissions statistically. Long-term
investment in energy-intensive infrastructure projects increases emissions
[57]. However, infrastructure investment decisions must include environmental
sustainability and economic growth tradeoffs. Green
infrastructure solutions that reduce emissions and optimize resource
utilization are also needed.
Comparing
short-run and long-run coefficients may reveal a lot about evolving
correlations and impacts on transportation emissions. AEU and INVEST reduce
emissions, but their effects may vary, highlighting the complexities of energy
transitions and infrastructure development. Comparing the short- and long-term
relationships between DD, DRO, and emissions shows that environmental impacts
must take temporal dynamics and socioeconomic context into consideration. These
comparisons have improved our understanding of carbon emission drivers, and we
need sustainable development plans and integrated policy frameworks to
alleviate environmental concerns and improve people's economic and social
situations. DRO's relevance and negative coefficient will reduce carbon emissions
over time. Optimization of delivery routes reduces trip time and improves
distribution efficiency. Chain supermarket route planning may reduce travel and
carbon emission costs [58]. Resource pooling and horizontal cooperation in
joint delivery models may minimize operational costs and carbon emissions for
express enterprises. This boosts client satisfaction. Table 6 displays Granger
causality estimates for convenience.
Tab. 6
Granger Causality Estimates
|
Null Hypothesis |
F-Statistic |
Prob. |
|
DD ®AEU |
4.75328 |
0.0205 |
|
DRO
®DD |
9.41670 |
0.0013 |
|
DD ®DRO |
4.38472 |
0.0264 |
|
INVEST
® DD |
4.07474 |
0.0328 |
|
DD ®SCE |
9.92583 |
0.0010 |
→ shows
one-way linkages between the variables
DD
has a unidirectional causal association with AEU (F-statistic = 4.753 and
p-value of 0.020). Changes in delivery density influence both delivery service
demand and delivery company operations; hence, they affect alternative energy
consumption. [59] suggest that collaborative distribution and speedy delivery
utilizing new energy vehicles may reduce carbon emissions and increase vehicle
load rates. There is a bidirectional relationship between DD and DRO, which
implies that changes in DRO may precede and influence changes in DD;
conversely, changes in DD may precede and have significant predictive power
over changes in DRO. [60] found that customer demand and delivery patterns
optimize routes. Changes in infrastructure investment may anticipate or impact changes
in DD (a directional relationship between the two variables), as shown by the
relationship between INVEST Granger and DD (F-statistic = 4.074, p = 0.032).
Infrastructure development increases product and service dispersion, lowering
transportation costs [61]. Finally, considering that DD Granger induces SCE, we
show that DD changes may precede and strongly predict SCE changes (F-statistic
9.925, p-value 0.001). Changes in DD may impact or precede changes in SCE,
suggesting a one-way directional relationship between them.
The
IRF projections in Table 7 demonstrate that AEU will cut transport emissions
from 2026 to 2031.
Tab. 7
IRF Estimates
|
Response of TCO2 |
||||||
|
Years |
TCO2 |
AEU |
DD |
DRO |
INVEST |
SCE |
|
2024 |
0.748640 |
0 |
0 |
0 |
0 |
0 |
|
2025 |
0.619255 |
0.295877 |
-0.026535 |
-0.304699 |
0.368766 |
0.125223 |
|
2026 |
0.433358 |
-0.022265 |
0.061183 |
-0.289095 |
0.458233 |
0.148294 |
|
2027 |
0.257253 |
-0.156756 |
0.117107 |
-0.252786 |
0.492535 |
-0.093380 |
|
2028 |
0.093947 |
-0.065828 |
0.139197 |
-0.249090 |
0.544305 |
-0.185876 |
|
2029 |
-0.003293 |
-0.111272 |
0.138362 |
-0.176705 |
0.503714 |
-0.058373 |
|
2030 |
-0.076204 |
-0.179523 |
0.088816 |
-0.083803 |
0.371873 |
0.003912 |
|
2031 |
-0.150600 |
-0.100281 |
0.047693 |
-0.024197 |
0.265060 |
-0.041077 |
|
2032 |
-0.185757 |
0.014358 |
0.074376 |
0.037336 |
0.224058 |
-0.036566 |
|
2033 |
-0.174883 |
0.051483 |
0.139161 |
0.121794 |
0.192371 |
0.023943 |
Delivery
density and infrastructure investment are expected to increase transport
emissions over the next 10 years. DRO is initially projected to decrease
transport emissions from 2025 to 2031, but it is expected to rise afterward.
SCE is expected to decrease transport emissions from 2027 onward until 2032,
after which it is expected to increase. As the analysis progresses, the
responses of the variables to shocks evolve, indicating a complex interplay
between carbon emissions and the factors influencing them. These insightful
outcomes contribute to a deeper understanding of the dynamics within the system
and can inform strategies for managing and mitigating carbon emissions
effectively. Table 8 shows the VDA estimates.
Tab. 8
VDA Estimates
|
Variance Decomposition of TCO2 |
||||||||
|
Years |
S.E. |
TCO2 |
AEU |
DD |
DRO |
INVEST |
SCE |
|
|
2024 |
0.748640 |
100 |
0 |
0 |
0 |
0 |
0 |
|
|
2025 |
1.129910 |
73.93601 |
6.857031 |
0.055149 |
7.272011 |
10.65157 |
1.228228 |
|
|
2026 |
1.335769 |
63.42828 |
4.934160 |
0.249258 |
9.887309 |
19.38968 |
2.111317 |
|
|
2027 |
1.484573 |
54.35303 |
5.109516 |
0.824039 |
10.90395 |
26.70454 |
2.104929 |
|
|
2028 |
1.621529 |
45.89502 |
4.447664 |
1.427618 |
11.49954 |
33.65178 |
3.078378 |
|
|
2029 |
1.717338 |
40.91730 |
4.385060 |
1.921883 |
11.31096 |
38.60479 |
2.860013 |
|
|
2030 |
1.772146 |
38.61041 |
5.144233 |
2.056021 |
10.84576 |
40.65725 |
2.686330 |
|
|
2031 |
1.802233 |
38.03032 |
5.283524 |
2.057977 |
10.50469 |
41.47415 |
2.649337 |
|
|
2032 |
1.827900 |
38.00250 |
5.142352 |
2.166149 |
10.25347 |
41.82006 |
2.615472 |
|
|
2033 |
1.856404 |
37.73195 |
5.062564 |
2.662086 |
10.37145 |
41.61954 |
2.552408 |
|
The results suggest that infrastructure investment
is likely to exert the greatest variance shocks on transport emissions, with a
magnitude of 41.619%, followed by DRO, AEU, DD, and SCE with variance shocks of
10.371%, 5.062%, 2.662%, and 2.552%, respectively, for the next 10 years. These
variables demonstrate varying degrees of impact, with infrastructure investment
and delivery route optimization showing particularly notable contributions. As
we progress towards later periods, the contributions of alternative energy use,
delivery density, and supply chain efficiency also become more apparent.
However, transport emissions remain the primary driver of variance throughout
the analyzed period, indicating their central role in
the dynamics of the system.
5. CONCLUSIONS
AND POLICY RECOMMENDATIONS
The
escalating environmental issues and ecological concerns require strict control
over transport emissions. Our study on sustainable last-mile delivery
optimization represents a critical stride toward fostering environmentally
responsible transportation practices. The results show a strong negative
correlation between CO2 and DD, underscoring the potential for
emission reduction through increased delivery density, while the significant
negative correlation between CO2 and DRO highlights the importance
of efficient route planning. However, the weak positive correlation between CO2
and INVEST suggests the necessity for strategic environmental interventions
alongside investment efforts. Furthermore, the Granger causality estimates
unveil directional relationships among key variables, emphasizing the influence
of delivery density on alternative energy use and the reciprocal nature of the
relationship between delivery density and route optimization. The significant
relationship between infrastructure investment and delivery density highlights
the role of infrastructure development in optimizing last-mile delivery
operations. Additionally, fluctuations in delivery density significantly
influence supply chain efficiency, underlining the importance of targeted interventions
to enhance operational efficiencies and minimize environmental impacts. Through
proactive measures and technological innovations, stakeholders can navigate
towards a greener, more efficient last-mile delivery system, aligning with
broader sustainability goals while fostering economic prosperity and societal
well-being.
To increase urban delivery efficiency and
sustainability, a comprehensive collection of short-, medium--, and long-term
policy statements may be created. Offering incentives for delivery route
merging is needed to encourage logistics companies to cooperate immediately.
Price schemes that incentivize off-peak delivery may achieve this aim while
lowering peak-hour emissions and congestion. Advanced routing algorithms and
real-time tracking technologies will improve route planning and resource use,
while urban loading and unloading zones will ease last-mile delivery. Delivery
drivers may reduce fuel use and emissions by taking eco-driving courses and
obtaining incentives. Land-use laws and
zoning constraints should soon favor mixed-use
buildings and higher-density cities, making neighborhoods
more walkable and reducing long-distance commuting. Public-private partnerships
that invest in shared mobility solutions may diversify transport options and
reduce last-mile delivery dependency on individual car ownership. Congestion
pricing and low-emission zones encourage cleaner mobility by charging vehicles
for their emissions. Real-time traffic data in delivery management systems may
improve route choices, delivery delays, and emissions. Regulations should
support transit-oriented development and pedestrian-friendly infrastructure to
keep cities small and sustainable. Public transit improvements may reduce
delivery reliance on private cars and improve mobility. Telecommuting and
flexible scheduling may alleviate travel congestion and promote sustainable
delivery. Encouraging innovative ecosystems to develop autonomous vehicles and
drones will transform last-mile logistics and reduce emissions.
Digital infrastructure investments and smart
city infrastructure interoperability standards may optimize delivery routes and
demand pattern prediction. To reinforce transport networks against
climate-related hazards, a circular economy model and infrastructure investment
objectives must be linked to adaptation goals. Circular economy principles
across the supply chain may optimize resource use and minimize environmental
impact. This will improve urban delivery sustainability.
References
1.
Chen S.,
L. Wu, K. K. Ng, W. Liu, K. Wang. 2024. "How Airports Enhance the
Environmental Sustainability of Operations: A Critical Review from the
Perspective of Operations Research." Transportation Research Part E:
Logistics and Transportation Review 183: 103440.
2.
Sumitkumar
R., A. S. Al-Sumaiti. 2024. "Shared Autonomous Electric Vehicle: Towards
Social Economy of Energy and Mobility from Power-Transportation Nexus
Perspective." Renewable and Sustainable Energy Reviews 197: 114381.
3.
Ohsugi
S., N. Koshizuka. 2018. "Delivery Route Optimization through Occupancy
Prediction from Electricity Usage." Proceedings of the 2018 IEEE 42nd
Annual Computer Software and Applications Conference (COMPSAC) 1: 842-849. IEEE.
4.
Riazi S.
2020. Energy and Route Optimization of Moving Devices. Doctoral
Dissertation, Chalmers Tekniska Högskola (Sweden).
5.
Wygonik
E., A. Goodchild. 2017. "Evaluating the Impacts of Density on Urban Goods
Movement Externalities." Journal of Urbanism: International Research on
Placemaking and Urban Sustainability 10(4): 487-499.
6.
Dulal H.
B., G. Brodnig, C. G. Onoriose. 2011. "Climate Change Mitigation in the
Transport Sector through Urban Planning: A Review." Habitat
International 35(3): 494-500.
7.
Liu D.,
H. Yang, X. Mao, V. Antonoglou, E. I. Kaisar. 2023. "New Mobility-Assist
E-Grocery Delivery Network: A Load-Dependent Two-Echelon Vehicle Routing
Problem With Mixed Vehicles." Transportation Research Record
2677(1): 294-310.
8.
Oskarbski
J., D. Kaszubowski. 2018. "Applying a Mesoscopic Transport Model to
Analyse the Effects of Urban Freight Regulatory Measures on Transport Emissions – An Assessment." Sustainability 10(7): 2515.
9.
Álvarez
Pelegry E., M. Larrea Basterra. 2021. "Alternative Energies in Transport
in the Context of Energy Transitions." In: The Role
of the Electric Vehicle in the Energy Transition: A Multidimensional Approach: 157-192.
10.
Wismans
J., M. Grahn, I. Denbratt. 2015. "Low-Carbon Transport – Health and
Climate Benefits." Background Paper for the Intergovernmental Ninth
Regional Environmentally Sustainable Transport (EST) Forum in Asia: 17-20.
11.
Tien N. H., D. B. H. Anh, T. D. Thuc. 2019. Global
Supply Chain and Logistics Management. Available at: https://www.researchgate.net/publication/338570722_Global_Supply_Chain_And_Logistics_Management.
12.
Verma S. K., R. Verma, B. K. Singh, R. S. Sinha.
2024. "Management of Intelligent Transportation Systems and Advanced
Technology." In: Upadhyay R. K., S. K. Sharma, V. Kumar (eds), Intelligent
Transportation System and Advanced Technology, Energy, Environment,
and Sustainability. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-97-0515-3_8.
13.
Cheng S., F. Zhang, X. Chen. 2024. "Optimal
Contract Design for a Supply Chain with Information Asymmetry under Dual
Environmental Responsibility Constraints." Expert Systems with
Applications 237: 121466.
14.
Liao Z., Z. Deng, K. Jian, Y. Guo. 2019.
"Research on the Optimization of Express Delivery Route of Logistics Base
under the Constraint of Carbon Quota." Journal of Physics: Conference
Series 1423(1): 012044. IOP Publishing.
15.
Chen Y., S. Hu, Y. Zheng, S. Xie, Q. Yang, Y.
Wang, Q. Hu. 2024. "Coordinated Optimization of Logistics Scheduling and
Electricity Dispatch for Electric Logistics Vehicles Considering Uncertain
Electricity Prices and Renewable Generation." Applied Energy 364:
123147.
16.
Dong G., F. Witlox, Y.
Bie. 2024. "Decentralizing E-Bus Charging Infrastructure Deployment Leads
to Economic and Environmental Benefits." Communications in
Transportation Research 4: 100139.
17.
Zheng J., X. Shi, Z. Zhang. 2024.
"Assessing Feasibility of Direct Measurement Technology for Monitoring
Carbon Emissions in Ports." Communications in Transportation Research
4: 100132.
18.
Figliozzi M. A. 2020. "Carbon Emissions
Reductions in Last Mile and Grocery Deliveries Utilizing Air and Ground
Autonomous Vehicles." Transportation Research Part D: Transport and
Environment 85: 102443.
19.
Zhong Y., X. Bai, W. Huang, L. Liu, R. Zhu, Y.
Zhai, S. Cui et al. 2023. "Carbon Emissions from Urban Takeaway Delivery
in China." Research Square. Available at: https://www.researchsquare.com/article/rs-2924102/v1.
20.
Nuţă F.
M., A. Sharafat, O. J. Abban, I. Khan, M. Irfan, A. C. Nuţă,
M. Asghar et al. 2024. "The Relationship Among Urbanization, Economic
Growth, Renewable Energy Consumption, and Environmental Degradation: A
Comparative View of European and Asian Emerging Economies." Gondwana
Research 128: 325-339.
21.
Al-Madani M. H. M., Y. Fernando, M. Iranmanesh, M. K. Lim, M. L. Tseng. 2024. "Renewable
Energy Supply Chain in Malaysia: Fostering Energy Management Practices and
Ecological Performance." Renewable Energy 120441.
22.
Hong Z., K. Xiao. 2024. "Digital Economy
Structuring for Sustainable Development: The Role of Blockchain and Artificial
Intelligence in Improving Supply Chain and Reducing Negative Environmental
Impacts." Scientific Reports 14(1): 3912.
23.
Sun C., S. Li, Q. Luo, J. Zhao, Z. Qi. 2023.
"Research on the Efficiency of Urban Infrastructure Investment under the
Constraint of Carbon Emissions: Taking Provincial Capitals in China as an
Example." Sustainability 15(12): 9305.
24.
Dzator
J., A. O. Acheampong, M. Dzator. 2021. "The
Impact of Transport Infrastructure Development on Carbon Emissions in OECD
Countries." Environmental Sustainability and Economy: 3-17.
Elsevier.
25.
Chu L. K., D. P. Hoang, H. T. Le. 2024.
"The Interaction of Public–Private Partnership Investment in Energy and
Geopolitical Risk in Influencing Carbon Dioxide Emissions in E7
Countries." Environment, Development and Sustainability. DOI: https://doi.org/10.1007/s10668-024-04590-w.
26.
Awewomom
J., F. Dzeble, Y. D. Takyi, W. B. Ashie, E. N. Y. O. Ettey, P. E. Afua, O. Akoto, et al. 2024. "Addressing
Global Environmental Pollution Using Environmental Control Techniques: A Focus
on Environmental Policy and Preventive Environmental Management." Discover
Environment 2(1): 8.
27.
Laynes-Fiascunari V.,
E. Gutierrez-Franco, L. Rabelo, A. T. Sarmiento, G. Lee. 2023. "A Framework
for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social
Media Analytics and Deep Learning Techniques." Applied Sciences
13(10): 5888.
28.
Chen Y. P. V., Z. Zhuo, Z. Huang, W. Li. 2022.
"Environmental Regulation and ESG of SMEs in China: Porter Hypothesis
Re-Tested." Science of the Total Environment 850: 157967.
29.
Liu B., X. Yang, J. Zhang. 2024. "Nonlinear
Effect of Industrial Robot Applications on Carbon Emissions: Evidence from
China." Environmental Impact Assessment Review 104: 107297.
30.
Assis T. F. D., V. H. S. D. Abreu, M. G. D.
Costa, M. D. A. D’Agosto. 2022. "Methodology for Prioritizing Best
Practices Applied to the Sustainable Last Mile – The Case of a Brazilian Parcel
Delivery Service Company." Sustainability 14(7): 3812.
31.
Ayu T. 2021. "Optimizing the Heterogeneous
Fleet Vehicle Routing Problem with Time Window on Urban Last Mile
Delivery." In IOP Conference Series: Earth and Environmental Science
830(1): 012100. IOP Publishing.
32.
Mucowska M.
2021. "Trends of Environmentally Sustainable Solutions of Urban Last-Mile
Deliveries on the E-Commerce Market – A Literature Review." Sustainability
13(11): 5894.
33.
Bergmann F. M., S. M. Wagner, M. Winkenbach. 2020. "Integrating First-Mile Pickup and
Last-Mile Delivery on Shared Vehicle Routes for Efficient Urban E-Commerce
Distribution." Transportation Research Part B: Methodological
131: 26-62.
34.
Zhao H., W. Liu, B. Zhou, Z. W. Liu, J. X. Gao,
H. Y. Wang, C. N. Xie. 2022. "The Technical Proposal of Energy
Control System for Renewable Energy Usage." Journal of Physics:
Conference Series 2276(1): 012003. IOP Publishing.
35.
Liu D., Z. Deng, X. Mao, Y. Yang, E. I. Kaisar.
2020. "Two-Echelon Vehicle-Routing Problem: Optimization of Autonomous
Delivery Vehicle-Assisted E-Grocery Distribution." IEEE Access 8:
108705-108719.
36.
Dündar H., M. Ömürgönülşen,
M. Soysal. 2021. "A Review on Sustainable Urban Vehicle Routing." Journal
of Cleaner Production 285: 125444.
37.
Büyüközkan
G., D. Uztürk. 2020. "A Hybrid Methodology for
Last Mile Delivery Strategy and Solution Selection at Smart Cities." Transactions
on Engineering Technologies: World Congress on Engineering 2019: 217-231.
Singapore: Springer Singapore.
38.
Bertarelli S., C. Lodi. 2019.
"Heterogeneous Firms, Exports and Pigouvian Pollution Tax: Does the
Abatement Technology Matter?" Journal of Cleaner Production 228:
1099-1110.
39.
Gaffar M. R., N. Hardiyanto,
W. Rafdinal. 2022. "Pigouvian Tax as a Support
System for Economic Development of Indonesia." Accounting and Finance
Studies 2(3): 131-140.
40.
Hua Y., S. Gao, W. Li, B. Jin, X. Wang, H. Zha. 2023. "Learning
Optimal 'Pigovian Tax' in Sequential Social
Dilemmas." arXiv preprint
arXiv:2305.06227.
41.
Hamaguchi Y. 2023. "Environmental Tax
Evasion as a Determinant of the Porter and Pollution Haven Hypotheses in a
Corrupt Political System." Economic Analysis and Policy 79: 610-633.
42.
Wu G., M. Sun, Y. Feng. 2024. "How Does the
New Environmental Protection Law Affect the Environmental Social Responsibility
of Enterprises in Chinese Heavily Polluting Industries?" Humanities
and Social Sciences Communications 11(1): 1-14.
43.
Jin C., F. S. Tsai, Q. Gu, B. Wu. 2022.
"Does the Porter Hypothesis Work Well in the Emission Trading Schema
Pilot? Exploring Moderating Effects of Institutional Settings." Research
in International Business and Finance 62: 101732.
44.
World Bank. 2023. World Development
Indicators. Washington, D.C.: World Bank.
45.
Shahbaz M., M. W. Zafar, M. Hussain. 2020.
"Challenges and Opportunities of Last-Mile Logistics in Developing
Economies: A Case Study of Pakistan." International Journal of
Logistics Systems and Management 35(4): 508-525.
46.
Nawaz A., M. Shafiq, I. Ali, X. Zhang. 2020.
"A Review on Emerging Trends, Issues, and Challenges in Last-Mile
Delivery." Transportation Research Part E: Logistics and
Transportation Review 140: 102021.
47.
Khan A. U., Y. H. Akbar. 2019. "Sectoral
Analysis of Pakistan’s Economy: Comparative Analysis Using an Input-Output
Model." The Pakistan Development Review 58(4): 651-676.
48.
Hameed Z., A. Abbas, K. U. Rehman. 2020.
"Role of CPEC in Enhancing Regional Connectivity and Economic Cooperation:
A Perspective from Pakistan." China Economic Journal 13(3): 257-276.
49.
Mehmood K., A. Khan, A. Raza, M. T. Majeed.
2020. "Factors Affecting Consumer Preferences: A Comparative Study of
Pakistan and China." Paradigms 14(2): 144-150.
50.
Pesaran M.
H., Y. Shin, R. P. Smith. 1999. "Pooled Mean Group Estimation of Dynamic
Heterogeneous Panels." Journal of the American Statistical Association
94(446): 621-634.
51.
Pesaran M.
H., Y. Shin, R. J. Smith. 2001. "Bounds Testing Approaches to the Analysis
of Level Relationships." Journal of Applied Econometrics 16(3):
289-326.
52.
Raihan A. 2023. "The Dynamic Nexus Between
Economic Growth, Renewable Energy Use, Urbanization, Industrialization,
Tourism, Agricultural Productivity, Forest Area, and Carbon Dioxide Emissions
in the Philippines." Energy Nexus 9: 100180.
53.
Labarthe O., G. Ahmadi, W. Klibi,
J. C. Deschamps, B. Montreuil. 2024. "A Sustainable On-Demand Urban
Delivery Service Enabled by Synchromodality and
Synergy in Passenger and Freight Mobility." Transportation Research
Part C: Emerging Technologies 161: 104544.
54.
Rohner P. T., J. A. Jones, A. P. Moczek. 2024.
"Plasticity, Symbionts and Niche Construction Interact in Shaping Dung
Beetle Development and Evolution." Journal of Experimental Biology
227(Suppl_1): jeb245976.
55.
Hassan Q., P. Viktor, T. J. Al-Musawi, B. M.
Ali, S. Algburi, H. M. Alzoubi, et al. 2024.
"The Renewable Energy Role in the Global Energy Transformations." Renewable
Energy Focus 48: 100545.
56.
Pan X., D. Lu, N. Li. 2024. "Route
Planning of Supermarket Delivery Through Third-Party Logistics Considering
Carbon Emission Cost." Mathematics 12(7): 1002.
57.
Zhang Q., J. Li, Q. Kong, H. Huang. 2024.
"Spatial Effects of Green Innovation and Carbon Emission Reduction in
China: Mediating Role of Infrastructure and Informatization." Sustainable
Cities and Society: 105426.
58.
Olarewaju T., S. Dani, C. Obeng-Fosu, T. Olarewaju, A. Jabbar. 2024. "The Impact of Climate
Action on the Financial Performance of Food, Grocery, and Supermarket Retailers
in the UK." Sustainability 16(5): 1785.
59.
Mohammed A., O. Saif, M. Abo-Adma, A. Fahmy, R. Elazab. 2024. "Strategies and Sustainability in Fast
Charging Station Deployment for Electric Vehicles." Scientific Reports
14(1): 283.
60.
Komarudin,
A. N. Abyantara. 2019. "Package Delivery Route
Optimization with Tight Travel Time." Proceedings of the 5th
International Conference on Industrial and Business Engineering: 39-43.
61.
Ayesu E.
K., K. A. B. Boateng. 2024. "Estimating the Impact of Container Port
Throughput on Employment: An Analysis for African Countries with
Seaports." Journal of Shipping and Trade 9(1): 8.
Received 02.05.2025; accepted in revised form 30.07.2025
Scientific Journal of Silesian
University of Technology. Series Transport is licensed under a Creative
Commons Attribution 4.0 International License